AlphaGo's mid game today was really strange. Many experts have praised Lee's move 78 as a "divine-inspired" move.
Add to that the moves where AlphaGo basically threw away stones by adding to formations that would be removed from the table. Even I, a complete, lousy, amateur, could see that they were a mistake.
To be fair, those moves were made when AlphaGo was already behind. It's just not any good at dealing with being that far back. The AI just has no concept of what to do while behind: What a human would do is to go for positions that are very complicated, making the chances of sloppy play much higher. Instead, it makes moves that have to be answered in only one or two ways, but that are very easy to read by even an amateur human.
Training an ai to make good play in a bad situation would require it to train in ways that are very different than the AlphaGo vs AlphaGo training that it spent a lot of time doing. And why do that, instead of trying to make itself good while the game is even, or when it's winning?
It's a bit like how it's different to train in chess to play in pro games, vs training to hustle amateurs in the park: You are not making the best move, but a good move that will confuse the opponent the most. You are trying to exploit a bad opponent: Very different play.
Add to that the moves where AlphaGo basically threw away stones by adding to formations that would be removed from the table. Even I, a complete, lousy, amateur, could see that they were a mistake.